论文标题
Deepopht：通过深层模型和视觉解释的视网膜图像的医学报告生成
DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation
论文作者
论文摘要
在这项工作中，我们提出了一种基于AI的方法，该方法旨在改善常规的视网膜疾病治疗程序，并帮助眼科医生提高诊断效率和准确性。所提出的方法由基于深神经网络（基于DNN的）模块组成，包括视网膜疾病标识符和临床描述生成器以及DNN视觉解释模块。为了训练和验证基于DNN的模块的有效性，我们提出了一个大规模的视网膜疾病图像数据集。同样，作为基础真理，我们提供了一个由眼科医生手动标记的视网膜图像数据集，以定性地显示，提出的基于AI的方法是有效的。通过我们的实验结果，我们表明所提出的方法在定量和定性上是有效的。我们的方法能够创建有意义的视网膜图像描述和视觉解释在临床上相关。
In this work, we propose an AI-based method that intends to improve the conventional retinal disease treatment procedure and help ophthalmologists increase diagnosis efficiency and accuracy. The proposed method is composed of a deep neural networks-based (DNN-based) module, including a retinal disease identifier and clinical description generator, and a DNN visual explanation module. To train and validate the effectiveness of our DNN-based module, we propose a large-scale retinal disease image dataset. Also, as ground truth, we provide a retinal image dataset manually labeled by ophthalmologists to qualitatively show, the proposed AI-based method is effective. With our experimental results, we show that the proposed method is quantitatively and qualitatively effective. Our method is capable of creating meaningful retinal image descriptions and visual explanations that are clinically relevant.
